The topic of positioning in dense forest has attracted researchers’ attention in the last few decades. Currently, accurate and robust positioning in complex forest environment is still not available by using standalone GNSS, and it is a major drawback for forestry automation. Laser scanning-based SLAM demonstrated its potential as an alternative positioning solution in dense boreal forest environment. However, it has difficulties in an open forest due to lack of features. In this paper, we propose a map-matching method to cover that gap. We utilize the existing forest map information, such as the center and radius of stems, and match it with the observed point cloud, which helps mitigate position errors. Field test results show that it is feasible to filter out the noisy measurements and reliably detect stem features in a boreal forest.